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pro vyhledávání: '"Franc, Alain"'
Autor:
Franc, Alain
These notes are an overview of some classical linear methods in Multivariate Data Analysis. This is a good old domain, well established since the 60's, and refreshed timely as a key step in statistical learning. It can be presented as part of statist
Externí odkaz:
http://arxiv.org/abs/2209.13597
Autor:
Franc, Alain
A tensor is a multi-way array that can represent, in addition to a data set, the expression of a joint law or a multivariate function. As such it contains the description of the interactions between the variables corresponding to each of the entries.
Externí odkaz:
http://arxiv.org/abs/2201.07473
This paper presents an extension of Correspondence Analysis (CA) to tensors through High Order Singular Value Decomposition (HOSVD) from a geometric viewpoint. Correspondence analysis is a well-known tool, developed from principal component analysis,
Externí odkaz:
http://arxiv.org/abs/2111.04343
Distance Geometry Problem (DGP) and Nonlinear Mapping (NLM) are two well established questions: Distance Geometry Problem is about finding a Euclidean realization of an incomplete set of distances in a Euclidean space, whereas Nonlinear Mapping is a
Externí odkaz:
http://arxiv.org/abs/1810.08661
Autor:
Blanchard, Pierre, Chaumeil, Philippe, Frigerio, Jean-Marc, Rimet, Frédéric, Salin, Franck, Thérond, Sylvie, Coulaud, Olivier, Franc, Alain
We have designed a new efficient dimensionality reduction algorithm in order to investigate new ways of accurately characterizing the biodiversity, namely from a geometric point of view, scaling with large environmental sets produced by NGS ($\sim 10
Externí odkaz:
http://arxiv.org/abs/1803.02272
Autor:
Peyrard, Nathalie, Cros, Marie-Josée, de Givry, Simon, Franc, Alain, Robin, Stéphane, Sabbadin, Régis, Schiex, Thomas, Vignes, Matthieu
Probabilistic graphical models offer a powerful framework to account for the dependence structure between variables, which is represented as a graph. However, the dependence between variables may render inference tasks intractable. In this paper we r
Externí odkaz:
http://arxiv.org/abs/1506.08544
Publikováno v:
Journal of Biogeography, 2018 Jul 01. 45(7), 1582-1592.
Externí odkaz:
https://www.jstor.org/stable/26628965
Autor:
Franc, Alain
Publikováno v:
[Research Report] 9488, Inria Bordeaux Sud-Ouest. 2023, pp.99
These notes are an overview of some classical linear methods in Multivariate Data Analysis. This is a good old domain, well established since the 60's, and refreshed timely as a key step in statistical learning. It can be presented as part of statist
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______165::bf0069b17ea026d560d4730d91c97f6b
https://inria.hal.science/hal-03784623v3/document
https://inria.hal.science/hal-03784623v3/document
Autor:
Agullo, Emmanuel, Buttari, Alfredo, Coulaud, Olivier, Eyraud-Dubois, Lionel, Faverge, Mathieu, Franc, Alain, Guermouche, Abdou, Jego, Antoine, Peressoni, Romain, Pruvost, Florent
Publikováno v:
International Parallel and Distributed Processing Symposium
IPDPS 2023-37th International Parallel and Distributed Processing Symposium
IPDPS 2023-37th International Parallel and Distributed Processing Symposium, IEEE, May 2023, St. Petersburg, FL, United States. à paraître
IPDPS 2023-37th International Parallel and Distributed Processing Symposium
IPDPS 2023-37th International Parallel and Distributed Processing Symposium, IEEE, May 2023, St. Petersburg, FL, United States. à paraître
International audience; Dense matrix multiplication involving a symmetric input matrix (SYMM) is implemented in reference distributed-memory codes with the same data distribution as its general analogue (GEMM). We show that, when the symmetric matrix
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=od_______165::75fb8b0fa4d2c7380e6b27af586006c1
https://inria.hal.science/hal-04093162
https://inria.hal.science/hal-04093162
Publikováno v:
Journal of Applied Ecology, 2016 Jun 01. 53(3), 856-864.
Externí odkaz:
http://www.jstor.org/stable/43869716